The previous article introduces Object Detection. This article will introduce the concept of Image Segmentation, and explain how to train a custom image segmentation model using TensorFlow Object Detection API through cases, including data set collection and processing, TensorFlow Object Detection API installation, and model training. Mask R-CNN to be used in this article is an Instance Segmentation model. The installation of TensorFlow Object Detection API is consistent with the Object Detection, please refer to Previous, so I won't repeat it here. Note:!!! From here, please make sure to execute under the environment of conda od.

However, there are a few requirements that are to be satisfied by the user before using the API. The API uses the dataset in the tf record format. It is a binary format for representing the data. The API uses this format to speed up the training process. Tf record internally represents the data in a format that allows for parallel processing.

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

Along the way, I'll share personal commentary, stories from established deep learning practitioners, and code snippets. Let's start by looking at the common points that can fail a neural network. There are three categorical features here: Sex, Has_Masters, and Has_Bachelors. You may one-hot encode to better represent the relationship or you may just keep them as they are. There are two continuous features in the dataset: Age and Bounties. They vary largely in scale, so you would want to standardize their scales. There are several ways to initialize the weights in a neural network. You can start with all zeros (which isn't advisable, and we will see it a second), you can randomly initialize them, or you can choose a technique like Xavier initialization or He initialization.

In my previous blog, we have seen how the off-the-shelf Object Detection is applied in Enterprise context in conjunction with SAP Leonardo Machine Learning Foundation. Now we will have a close look at how to implement custom object detection with tensorflow for serving intelligent solutions, especially how to train a custom object detector with custom dataset, and provision as RESTful API running on SAP Cloud Platform, Cloud Foundry, which can be consumed by your intelligent solution through loosely-coupled HTTP(s). The TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. For more detail, you may refer to its official document. In order to train your own object detector, you need to prepare the dataset for training, including the images with the target objects, and labelling the object in the images.

This allows for more fine-grained information about the extent of the object within the box. So when would we need this extra granularity?

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

My first (at all!) post was devoted to 2 basic questions of training detection models using TensorFlow Object Detection API: how are negative examples mined and how the loss for training is chosen. This time I'd like to cover 3 more questions regarding the following: As before, I totally recommend to recap the SSD architecture features following the same links as were provided in my previous post. In SSD, there is no region-proposal step (in contrast with R-CNN models) and the set of regions to be considered by the model is completely predefined by the configuration. In short, the features from the feature-head of the network are passed to a pipeline of the detection blocks. Every detection block receives a reduced in spatial size tensor (which is still a somewhat representation of input image) and overlays it with a regular grid which nodes are later used as centers for the set of assumed bounding boxes.

A few weeks ago, Facebook open-sourced its platform for object detection research, which they are calling Detectron. Object detection, wherein a machine learning algorithm detects the coordinates of objects in images, remains an ongoing challenge. To find algorithms that provide both sufficient speed and high accuracy is far from a solved problem. Detectron's ostensible purpose is to move a bit closer to that goal using community-led contributions. Google did something similar last year when they released the Tensorflow Object Detection API, which Filestack utilizes for its object detection models, so we thought we'd take a look at Detectron and see how it compares.